import eda_utility as u
import predictive_utility as p
import pandas as pd
%matplotlib inline
# import datasets
calendar = pd.read_csv('Seattle_data/calendar.csv')
listings = pd.read_csv('Seattle_data/listings.csv')
reviews = pd.read_csv('Seattle_data/reviews.csv')
# EDA for seattle
# busy times
month_count = u.busiest_time(calendar, 'Seattle')
month_count
# Time series analysis
u.time_series_analysis(calendar, 'Seattle')
u.weekday_decomposition(calendar)
# Sentiment Analysis
sentiment_df = u.sentiment_analysis(reviews)
u.sentiment_distribution(sentiment_df)
u.sentiment_word_plots(sentiment_df)
# listing distribution visualization
# seattle location
seattle_location = [47.6062, -122.3321]
listing_map = u.listing_distribution_map(listings, seattle_location)
listing_map
neighbourhood_map = u.listing_count_map(listings, seattle_location)
neighbourhood_map